Water Quality Influences on Macrobrachium spp. Shrimp Abundance in the Rongkong Sub Watershed, Indonesia

2.3 Shrimp sampling and identification

Sampling of Macrobrachium spp. was conducted monthly with the assistance of local fishermen in the early morning using mini bamboo traps (Kopa’) baited with coconut or coconut tubers and submerged in the water. A purposive sampling method was applied, with samples collected from three predetermined river locations in the Rongkong Sub-Watershed: W, Salujambu, and P, each with several designated stations.

Collected samples were sorted by species and counted on-site. Specimens that were difficult to identify were placed in cool boxes and transported to the laboratory for further analysis. During the study period, 1,628 individuals were collected from W, 1,657 from Salujambu, and 1,568 from P. Morphological identification of Macrobrachium spp. was conducted at the Biology Laboratory of the Indonesian Institute of Sciences (LIPI), Cibinong, Bogor. DNA-based identification was conducted at the Indonesia Biodiversity Laboratory, South Denpasar, Bali, and Genetics Science, Jakarta.

Morphometric measurements followed manuals from previous studies [24, 25], including nine morphometric characters (total length, abdominal length, telson length, carapace length, carapace width, diagonal carapace length, first abdominal segment length, rostrum length, and second abdominal segment length) and two meristic characters (number of dorsal and ventral rostral teeth). Measurements were based on the protocol of the study [26], using a digital caliper with 0.01 mm accuracy. Sex determination was based on visual morphological differences, as documented in previous studies [27].

Morphologically identified freshwater shrimp samples were further confirmed using DNA barcoding methods. Abdominal muscle tissue was used for DNA extraction, conducted with the Qiagen DNeasy Blood and Tissue Kit (Germany), following the manufacturer's protocol. Genomic DNA was amplified using PCR targeting the COI gene region, with primers jgLCO1490 and jgHCO2198 [28]. Each PCR reaction contained: 4 µL of 10× PCR buffer (Applied Biosystems), 2.5 µL of 10 mM dNTPs, 1.2 µL of 10 mM of each primer, 2 µL of 25 mM MgCl₂, 0.125 µL of AmpliTaq Gold™ DNA polymerase (Applied Biosystems), and 14.5 µL of ddH₂O. The same primers were used for Macrobrachium mammillodactylus (MM), Macrobrachium idea (MI), Macrobrachium latidactylus (ML), and Macrobrachium esculentum (ME) in this study, as COI gene-based DNA barcoding studies often utilize the same primer set for multiple species within the same genus, provided the species are phylogenetically closely related [29].

The total reaction volume was 25 µL, including 1 µL of DNA template. PCR conditions were as follows: initial denaturation at 94 ℃ for 14 seconds, followed by 38 cycles of denaturation at 94 ℃ for 30 seconds, annealing at 50 ℃ for 30 seconds, and extension at 72 ℃ for 45 seconds, with a final extension at 72 ℃ for 5 minutes. PCR products were visualized on 1% agarose gel stained with Biotium DNA dye. Successful PCR products were sequenced at PT. Genetika Science Indonesia, Jakarta. The forward and reverse sequences were edited using Chromas (http://technelysium.com.au/wp/chromas/) and further processed using MEGA 7 software [30]. The resulting sequences were compared to reference sequences of the same genus in the NCBI GenBank database to determine identity (% identity and % query coverage).

2.4 Data analysis

Mean values, standard deviations, and species abundance graphs were calculated using Microsoft Excel 2010. Two-way analysis of variance (ANOVA) was used to evaluate differences in water quality parameters across seasons and stations. Cluster analysis was conducted to assess similarities among stations based on shrimp species abundance. A two-dimensional non-metric multidimensional scaling (nMDS) plot was used to visualize and verify the relationship between species abundance and water quality parameters. Pearson correlation coefficients (r) were calculated to assess relationships between water quality and shrimp abundance. These analyses were performed using IBM SPSS Statistics 27.

Principal Component Analysis (PCA) was employed to reduce environmental variables and identify relationships among sampling stations and seasons. Before PCA and CCA analyses, Pearson correlation analysis was conducted to identify highly correlated environmental variables. Variables with correlation coefficients ≥ 0.70 were considered potentially collinear and evaluated for exclusion to reduce redundancy among predictors [31]. Environmental variables with PCA loading scores greater than 0.60 were considered ecologically relevant and retained for subsequent CCA analysis [32]. Variables excluded from the final model were those contributing redundant information or showing limited ecological interpretability relative to shrimp distribution patterns.

Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests of sphericity were performed to evaluate sampling adequacy and dataset suitability for multivariate analysis. Bartlett’s test was significant (χ² = 79.548; p = 0.001), indicating that the correlation matrix was appropriate for PCA. However, the KMO value was relatively low (0.275), likely due to the limited sample size and high dimensionality of environmental variables.

Canonical Correspondence Analysis (CCA) was used to examine the influence of environmental parameters on shrimp abundance. Because the number of environmental variables should not exceed the number of samples, and the dependent variables included four shrimp species, only a maximum of seven environmental variables were retained to maintain model validity. Multicollinearity among retained variables was assessed using the Variance Inflation Factor (VIF), where values exceeding 5 indicated moderate multicollinearity and values above 10 indicated high multicollinearity [33]. PCA and CCA analyses were performed using RStudio.

4.1 Physicochemical parameters

Water quality plays a critical role in natural environments, as it helps organisms maintain optimal physiological conditions. Poor physicochemical characteristics can lead to reduced productivity and economic losses [34]. DO concentrations of 4–5 mg/L or higher are considered optimal for aquaculture production [35], whereas levels below 2.0 mg/L are generally associated with limited growth and increased mortality risk [36].

In this study, DO concentrations varied significantly across seasons. Higher DO levels during the rainy season compared to the dry season may be attributed to seasonal stratification, driven by temperature-dependent water density. This indicates that cooler water is more capable of retaining DO [37].

Species of Macrobrachium exhibit variable minimum and maximum temperature thresholds depending on life stage, with a tolerance range of 13–43 ℃ [38]. Temperature is a key factor regulating growth rate, feed intake, feed conversion, survival rate, and disease resistance in prawns [39]. In this study, water temperature was highest during Transition Season 2 (TS2).

According to Booker and Whitehead [40], there is an inverse relationship between river temperature and flow rate. Lower flow rates are typically associated with warmer water, especially when other factors such as season, solar radiation, air temperature, and soil temperature remain constant. Reduced flow leads to a disproportionate reduction in surface area because river width and flow decrease exponentially, resulting in narrower channels that tend to warm more quickly [41].

The higher water temperature observed in the Pombakka River may be explained by several interacting factors. River morphology, particularly depth, influences thermal dynamics, as shallow systems tend to warm more rapidly due to smaller water volumes that are more sensitive to atmospheric conditions. Low flow velocity further enhances heat accumulation through prolonged solar exposure. In addition, local environmental conditions, such as higher ambient temperatures and reduced riparian vegetation cover, can increase heat absorption. Anthropogenic activities, including agriculture and settlement development, may also contribute through thermal inputs and the removal of shading vegetation. Furthermore, elevated nutrient concentrations may stimulate algal photosynthesis, indirectly influencing thermal conditions. These combined factors likely account for the higher temperatures observed in the Pombakka River relative to other sites.

Seasonal changes were also found to affect water pH, as noted in other studies [42-44]. In this study, pH tended to be lower during the rainy season. This is likely due to increased surface runoff following rainfall events, which carry more organic material into rivers, thereby lowering pH values [45].

Natural factors such as rainfall, wind speed, and temperature also influence Secchi Disk Depth (SDD). Increased rainfall contributes to higher river runoff, which in turn transports more suspended solids like sediment into the water, resulting in reduced transparency [46]. The dry season can also affect water quality due to higher temperatures, longer water residence time, and increased nutrient concentrations—conditions that promote algal growth [47]. This growth reduces water clarity, further lowering SDD. Elevated nutrient levels during dry periods are linked to reduced dilution capacity of water bodies [48].

Natural surface water typically contains less than 5 mg/L of potassium (K) [49]. In this study, concentrations of K and NH₃ were generally higher during the rainy season. Increased rainfall may reduce potassium fixation and uptake in soils, thereby promoting leaching, runoff, and erosion that transport potassium into aquatic ecosystems [50]. Additionally, rainfall-induced runoff during the wet season mobilizes fertilizers from agricultural fields, increasing the concentrations of NO₃-N and NH₃-N in surface waters [51].

The NH₃ concentrations observed in this study were extremely low, indicating relatively low NH₃ contamination in the studied rivers. These low concentrations may reflect the oligotrophic characteristics of the freshwater system and limited organic waste inputs during the sampling period.

4.2 Variation in abundance of prawn species

In this study, variation in prawn species abundance was observed across different locations and seasons. Among the four species recorded, MM was the most dominant, contributing 67.99%, 66.34%, and 64.58% of total abundance in Pombakka, Waelawi, and the Salujambu River, respectively. This was followed by ME, with relative abundances of 18.49%, 19.74%, and 19.89% in Waelawi, Salujambu River, and Pombakka, respectively.

MM exhibited high abundance across all seasons, suggesting that the aquatic environment in these rivers provides suitable habitat conditions for this species [18]. A study by Jurniati et al. [52] on the morphometric and meristic characteristics of MM from the Waelawi, Salujambu, and Pombakka rivers concluded that the observed weights were higher than predicted values. The mean condition factor (Wr > 100 and k > 1) indicated adequate food availability and low predation pressure. MM has also been reported as a dominant species in the Petagas River, along with M. rosenbergii [53]. Specimens of MM were collected from freshwater-submerged tree roots in the lower river zones, with water temperatures around 26.5 ℃ [54].

ME has only been documented in Indonesia, the Philippines, and Taiwan [55]. It showed high abundance during TS2, WM, and TS1, but was absent during the east monsoon (EM). MI was found during TS2, WM, and EM, but was not recorded in TS1. ML was found during TS1 and EM, but was absent during TS2 and WM. These seasonal appearances may be explained by temporal ontogenetic separation, as prawn species exhibit distinct migration periods and recruitment patterns. Such seasonal distributions suggest interspecific strategies to reduce ecological interactions. Seasonal peaks in abundance are influenced by habitat suitability and environmental factors, each with varying strengths [18]. Seasonal change has been shown to significantly affect Macrobrachium distribution and habitat preferences [56].

In general, prawn abundance increased during the rainy season, consistent with findings by Kingdom et al. [57]. Increased freshwater flow into estuarine areas likely stimulates seasonal migration of prawns toward the sea, resulting in a preliminary rise in adult prawn abundance in adjacent marine waters. It is hypothesized that disturbance of estuarine sediments, routine burrowing, and respiration activities due to enhanced river discharge contribute to this emigration. This early increase in adult abundance enhances reproductive potential, while high rainfall indirectly supports juvenile recruitment, growth, and survival, ultimately increasing prawn populations in the following year.

Extended dry conditions negatively impact prawn populations and often result in reduced population sizes [58-60]. It was reported that a lower percentage of adult individuals of Atya lanipes and Xiphocaris elongata were gravid during drought conditions. Even in tropical rainforest biomes, prolonged drought can significantly alter aquatic communities through local density effects due to habitat shrinkage, ultimately resulting in long-term reproductive declines.

4.3 Influence of water quality on the abundance of prawn species

In tropical regions, key factors influencing the distribution and abundance of prawns include temperature, salinity, nutrient levels, and substrate composition [61]. In this study, MM was found across all seasons and sites, indicating a wide tolerance range to variations in water quality parameters. ME was more abundant in clearer waters, particularly during Transition Season 2 (TS2). This is similar to M. macrobrachion, which prefers higher water visibility and shows a positive correlation with Secchi disk transparency [57].

Secchi disk transparency influences light penetration in the water column, affecting photosynthesis and algal growth. Highly turbid waters limit light penetration, reducing both water temperature and photosynthetic activity. In such conditions, algal growth on the pond bottom tends to decrease [62, 63]. A Secchi disk depth between 30 and 60 cm is recommended for freshwater aquaculture [64].

The presence of ME was negatively correlated with the presence of ML [65]. We observed similar ontogenetic temporal partitioning between Farfantepenaeus paulensis and F. brasiliensis, with one species more abundant in summer and the other in autumn, likely due to differing migration and recruitment patterns. Such seasonal species distributions may represent strategies to reduce interspecific competition.

ML was mostly found at WEM, PEM, and SJEM and was negatively correlated with transparency. M. rosenbergii prefers turbid conditions, with phytoplankton contributing to the Secchi disk transparency. Phytoplankton abundance is strongly and heterogeneously related to orthophosphate levels [66, 67]. It is reported that prawns tend to prefer filamentous green algae and may avoid certain periphyton species such as diatoms. An optimal abundance and composition of phytoplankton contribute positively to water quality—not only through shading the benthos but also via oxygenation, NH₃ reduction, and by forming the base of the food web, which ultimately benefits prawn stock [68].

ML populations in the Gililana River were found on sandy, gravelly, and root-bound substrates. This species inhabits fast-flowing, shallow waters. According to research [69], ML was also found in the Andaman Islands in river depths of 2–3 cm with sandy and muddy substrates [70]. Its microhabitats include macrophytes, sago roots, leaf litter, and woody debris. Reported conditions include water temperature of 25–28 ℃, DO of 5.2–8.8 mg/L, and pH between 7.0 and 9.3, with substrates composed of mud and sand [71].

Macrobrachium idae (MI) was weakly correlated with potassium (K) (r = 0.309). MI was abundant during WM and absent in TS1, likely due to the gradual increase in K from TS1 to WM. MI is a freshwater prawn species with facultative euryhalinity and is typically more abundant during and shortly after the rainy season (October–February). Prawns are commonly found in estuarine niches, deep water, and mangrove areas. They migrate to brackish water zones for spawning and sometimes for larval hatching, with post-larval individuals returning to freshwater habitats [19, 20].

Potassium plays a key role in post-molting processes, which influence survival rates in Litopenaeus vannamei [72, 73]. The uptake of sodium (Na) ions is influenced by K, and magnesium (Mg) uptake is affected by calcium (Ca) concentrations [74]. Previous studies have reported that a Na ratio of 27:1 in freshwater media significantly influences the growth and survival of L. vannamei [75].

Ituarte found that Na⁺/K⁺-ATPase enzyme activity was present at all developmental stages of Palaemonetes argentinus, being lowest during early egg development (stage SI), highest at the late embryonic stage (SIII), and moderate during newly hatched zoea (ZI) and in adults [76].

Nevertheless, the present study was limited to three rivers and a one-year sampling period. Therefore, caution should be applied when generalizing the findings to other tropical river systems with different hydrological and ecological characteristics. Future studies involving broader geographical coverage and longer monitoring periods are needed to validate the applicability of these findings across larger watershed systems.

4.4 Management implications

The ecological relationships identified in this study provide important implications for watershed management and freshwater biodiversity conservation. Maintaining riparian vegetation, reducing agricultural fertilizer runoff, controlling sedimentation, and improving water transparency may help sustain suitable habitat conditions for Macrobrachium spp. These findings may support local river management policies and community-based conservation programs aimed at maintaining freshwater ecosystem health in the Rongkong Sub-Watershed.

Shrimp abundance is an important indicator for assessing the health and ecological balance of river ecosystems. In this study, variations in the abundance of Macrobrachium spp. indicate that changes in environmental conditions, particularly water quality parameters, can influence the distribution and population dynamics of the species. Given the high economic value of Macrobrachium spp., changes in habitat quality can impact not only the sustainability of aquatic ecosystems but also the social and economic aspects of communities dependent on these resources.

The results indicate that environmental factors such as Secchi disk transparency, DO, potassium, and nutrients are related to species distribution patterns. Fluctuations in these parameters likely affect habitat availability, food sources, and physiological processes that play a role in shrimp growth, development, and survival. Changing environmental conditions due to increased anthropogenic activity, land use changes, and increased pollutant loads have the potential to degrade natural habitat quality and alter the community structure of aquatic organisms.

These findings indicate that habitat conservation and management efforts need to consider the ecological characteristics and physiological tolerances of each Macrobrachium species. In line with the Law of the Republic of Indonesia No. 32 of 2009 concerning Environmental Protection and Management and supported by Government Regulation No. 22 of 2021 concerning the Implementation of Environmental Protection and Management, in general to specific, it mentions the efforts that can be made to maintain environmental quality and biodiversity. Such management can be directed at reducing sources of environmental stress, maintaining supportive habitat conditions, and regularly monitoring water quality and population dynamics. Furthermore, further research on the species' biology, life cycle, and response to environmental change is needed to support more effective and sustainable management strategies [77, 78].

This research is funded by the Indonesian Endowment Fund for Education (LPDP) on behalf of the Indonesian Ministry of Higher Education and managed under the EQUITY Program (Contract No. 4300/B3/DT.03.08/2025; No. 297/UN3/HK.07.00/2025; and No. 5537/B/UN3.LPPM/P.T.01.03/2025).

The authors would like to express their sincere gratitude to the “Indonesia Endowment Fund for Education (LPDP), the Ministry of Finance of the Republic of Indonesia, and the Ministry of Research, Technology, and Higher Education (Kemenristekdikti)” for providing the Beasiswa Unggulan Dosen Indonesia–Dalam Negeri (BUDI-DN) scholarship.